Self-Consistency In Sequential Decision-Making

Human decisions are rarely made in isolation. We typically have to make a sequence of decisions to reach a goal. Studies in economics and cognitive psychology have shown that making a decision may result in several biases in subsequent judgments. Similar biases have also recently been found in human percepts of low-level stimuli such as motion direction. What lacking is a principled framework that can account for several sequential dependencies between judgments. Towards that goal, in my thesis, I propose and experimentally test a self-consistent Bayesian observer model that assumes humans maintain self-consistency along the inference process. In Chapter 2, I first demonstrate that after having made a categorical decision on stimulus orientation, subjects’ estimate of the stimulus is systematically biased away from the decision boundary. Two additional experiments suggest that the bias occurs because subjects treat their first decision as a fact and use that to constrain the subsequent estimation. Model fit to the data in my experiments and data in previous studies show that the self-consistent Bayesian model can quantitatively account for human behaviors in a wide range of experimental settings. In Chapter 3, using the same decision-estimation tasks, I probed the post-decision sensory representation by providing feedback on the categorical decision. I found that subjects’ sensory representation is kept intact and the self-consistency is implemented by conditioning the prior distribution on the categorical decision. The results also suggest another interesting form of self-consistency when subjects’ decision was incorrect: they reconstructed the sensory measurement to make it consistent with the given feedback. In Chapter 4, I found that the choice-induced bias also occurs in human judgment of number. The bias is similar for both non-symbolic (cloud of dots) and symbolic (sequence of Arabic numerals) forms of number. Finally, I propose in the general discussion how the self-consistent Bayesian framework may account for other biases in sequential decision-making such as the halo effect and sunk-cost fallacy

Choice-dependent Perceptual Biases

The perceived motion direction of a dynamic random dot stimulus is systematically biased when preceded by a motion discrimination task (Jazayeri and Movshon, 2007). The biases were originally thought to occur because subjects mistakenly reuse the neural read-out optimized for the discrimination task when forming the percept (Fig.1a, Task-dependent model). In a series of experiments, we demonstrated that this explanation is incorrect and that the biases actually result from the conditioning of the percept on the preceding discrimination judgment (Fig1.b, Choice-dependent model). Experiment 1 was aimed at replicating the biases for an orientation stimulus. Subjects first indicated whether the stimulus orientation was clockwise (CW) or counter-clockwise (CCW) of a randomly chosen reference orientation. Subsequently they had to reproduce the stimulus orientation (Fig1.c). Experiment 2 was identical to Experiment 1 except that the total range of the stimulus were shown at the beginning of each trial. Experiment 3 was identical to Experiment 2 except that subjects were given the correct answer of the discrimination judgment (CW/CCW) and they instead performed an irrelevant decision task. Subjects’ estimates were systematically biased away from the decision boundary in Exp. 1(Fig.1 c). Similar biases occurred in Exp. 2 and 3. Because the task-dependent model is insensitive to the stimulus range and is contingent on subjects performing a discrimination, it cannot capture the shift of bias curves in Exp. 2 and in Exp. 3. In contrast, the choice-dependent model predicts all those features in the data assuming that subjects learned the narrower prior range and conditioned their percepts on the given decision outcome

Teaching Ways and Learning Ways Revisited

In learning and teaching of languages, numerous theories have been put forward. These theories, normally influenced by developments in the fields of linguistics and psychology, have inspired several approaches to the teaching of second and foreign languages. This paper revisits English language teaching approaches, both traditional and modern, as well as learning styles and teaching styles. Such learning style models as The Myers-Briggs Type Indicator (MBTI); Kolb’s Experiential Learning Model; the Felder-Silverman Learning Styles Model are explored in-depth in this paper. Keywords: teaching approach; learning style; teaching styl

Approaches to the assembly of potent therapeutic agents for the treatment of myotonic dystrophy

Myotonic dystrophy type 1 (DM1), the most common form of adult-onset muscular dystrophy, is an incurable neuromuscular disease. DM1 is caused by an expansion of the CTG repeat, whose RNA transcript sequesters the MBNL1 protein into nuclear foci, leading to the misregulation of various pre-mRNAs. Based on a published x-ray structure of a r(CUG)6 hairpin, a benzamidinium ligand was rationally designed and found to selectively bind rCUG repeats and inhibit the MBNL1-rCUGexp interaction with a low micro-molar inhibition potency. Thus, this bisamidinium RNA groove binder represented one of the most promising lead compounds for DM1 treatment. The development of the bisamidinium analogs containing functional substituents will be discussed in Chapter 2. Chapter 3 describes the development of a bisamidinium dimeric ligand as a possible compound for the treatment of DM1. This ligand reveals both the promise and challenge for the small molecule approach. The agent is a potent inhibitor of the MBNL1-rCUGexp both in vitro and in DM1 model cells, and is able to improve two distinct disease phenotypes in a DM1 Drosophila model. However, its relatively low maximum tolerated dose in mice and limited cell uptake provide insights into directions for future development. In addition, a powerful selection method that uses the target DNA or RNA to select their own binders will be discussed in Chapter 4. Compounds discovered using this method are able to undergo the alkyne-azide cycloaddition on the target DNA and RNA. The selectivity of these compounds along with their click products to other DNA/RNA sequences can also be readily accessed. The click products formed have better efficacy and can be used as multi-target agents

The process of evaluating students based on university program learning outcomes

As higher education tends to witness drastic changes, the quality of output is highly valued, which demands continuous innovation in the teaching process, especially on assessing students’ learning outcomes from the training program’s requirements. The paper outlines the relationship between student assessment and other components in the teaching process, performance indicator (PI) for the program learning outcomes, which refers to teaching, testing and assessment activities. At the same time, the article presents the process of setting up and evaluating the achievements of students through the method of PI evaluation based on the program’s learning outcomes. The current situation is that the learning outcomes of undergraduate training programs in various universities in the country tend to be developed in a general and highly integrated manner, thus widening the gap between the learning outcomes’ requirements and the actual teaching, testing, and evaluation activities of the module/ subject

Evaluation of genetic diversity and DNA fingerprinting of 19 standard reference rice varieties using SSR markers

Molecular markers are advanced-tools for identifying new varieties at DNA levels. According to the International Union for the Protection of New Varieties ofPlants,  new breeded varieties need to be tested for the Distinctness, Uniformity and Stability (DUS), before being recognized as the new ones. Traditional DUS criteria based on 62 - 65 morphological and biochemical characteristics, which evaluated on comparison of new varieties with 19 standard reference varieties for traits of interest.  Study on the genotypic polymorphism of 19 standard reference rice varieties provides genotypic information of these varieties for the evaluation of new rice varieties based on genotyping analysis.  The reference marker set (30 markers) was used to evaluate the genetic diversity and DNA fingerprinting of 19 standard reference rice varieties. The results showed the similarity coefficient of 19 varieties varied from 0.04 to 0.548. At the genetic similarity coefficient of 0.1, the 19 rice varieties divided into two main groups. Group one included 3 varieties: DH1, DH5, DH13. Group 2 included the remaining 16 varieties. Inside group two, phylogenetic tree divided into two main branches at the genetic similarity coefficient of 0.3. Branch 1 includes 5 varieties including DH2, DH6, DH10, DH11 and DH7. The 11 remaining varieties were in the branch 2. The most closely varieties were DH6 and DH10 with the genetic similarity coefficient of 0.548. This study shows that, the standard reference varieties have high uniformity and high genotypic polymorphism, could used for testing new varieties based on genotyping by DNA fingerprinting combining with phenotype

ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions

Mathematical questioning is crucial for assessing students problem-solving skills. Since manually creating such questions requires substantial effort, automatic methods have been explored. Existing state-of-the-art models rely on fine-tuning strategies and struggle to generate questions that heavily involve multiple steps of logical and arithmetic reasoning. Meanwhile, large language models(LLMs) such as ChatGPT have excelled in many NLP tasks involving logical and arithmetic reasoning. Nonetheless, their applications in generating educational questions are underutilized, especially in the field of mathematics. To bridge this gap, we take the first step to conduct an in-depth analysis of ChatGPT in generating pre-university math questions. Our analysis is categorized into two main settings: context-aware and context-unaware. In the context-aware setting, we evaluate ChatGPT on existing math question-answering benchmarks covering elementary, secondary, and ternary classes. In the context-unaware setting, we evaluate ChatGPT in generating math questions for each lesson from pre-university math curriculums that we crawl. Our crawling results in TopicMath, a comprehensive and novel collection of pre-university math curriculums collected from 121 math topics and 428 lessons from elementary, secondary, and tertiary classes. Through this analysis, we aim to provide insight into the potential of ChatGPT as a math questioner.Comment: Accepted at the 39th ACM/SIGAPP Symposium On Applied Computing (SAC 2024), Main Conferenc

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Factors influencing the occurrence of labour accidents in Vietnamese residential construction projects

There was a sharp increase in the annual injury rate and the annual death rate per 100,000 employees in the Vietnam construction industry (VCI) from 2000 to 2006. This study aimed to identify and analyze factors affecting the occurrence of labour accidents (OLA) in residential construction projects. A survey questionnaire was used to collect data from construction practitioners. The results show that labour accidents in the VCI resulted from factors related to unqualified construction resources, unsafe human behaviour and poor management. The results can be used to gain insight into the OLA in order to deal with high labour accident rate and to develop safety programs in Vietnam as well as in other developing countries

Isolation and characterization of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea)

<p>Abstract</p> <p>Background</p> <p>Cultivated peanut or groundnut (<it>Arachis hypogaea </it>L.) is the fourth most important oilseed crop in the world, grown mainly in tropical, subtropical and warm temperate climates. Due to its origin through a single and recent polyploidization event, followed by successive selection during breeding efforts, cultivated groundnut has a limited genetic background. In such species, microsatellite or simple sequence repeat (SSR) markers are very informative and useful for breeding applications. The low level of polymorphism in cultivated germplasm, however, warrants a need of larger number of polymorphic microsatellite markers for cultivated groundnut.</p> <p>Results</p> <p>A microsatellite-enriched library was constructed from the genotype TMV2. Sequencing of 720 putative SSR-positive clones from a total of 3,072 provided 490 SSRs. 71.2% of these SSRs were perfect type, 13.1% were imperfect and 15.7% were compound. Among these SSRs, the GT/CA repeat motifs were the most common (37.6%) followed by GA/CT repeat motifs (25.9%). The primer pairs could be designed for a total of 170 SSRs and were optimized initially on two genotypes. 104 (61.2%) primer pairs yielded scorable amplicon and 46 (44.2%) primers showed polymorphism among 32 cultivated groundnut genotypes. The polymorphic SSR markers detected 2 to 5 alleles with an average of 2.44 per locus. The polymorphic information content (PIC) value for these markers varied from 0.12 to 0.75 with an average of 0.46. Based on 112 alleles obtained by 46 markers, a phenogram was constructed to understand the relationships among the 32 genotypes. Majority of the genotypes representing subspecies <it>hypogaea </it>were grouped together in one cluster, while the genotypes belonging to subspecies <it>fastigiata </it>were grouped mainly under two clusters.</p> <p>Conclusion</p> <p>Newly developed set of 104 markers extends the repertoire of SSR markers for cultivated groundnut. These markers showed a good level of PIC value in cultivated germplasm and therefore would be very useful for germplasm analysis, linkage mapping, diversity studies and phylogenetic relationships in cultivated groundnut as well as related <it>Arachis </it>species.</p